Systematic evaluation of multiple NGS platforms for structural variants detection

Comparative evaluation of SNVs, indels, and structural variations detected with short- and long-read sequencing data

Short- and long-read sequencing technologies are routinely used to detect DNA variants, including SNVs, indels, and structural variations (SVs). However, the differences in the quality and quantity of variants detected between short- and long-read data are not fully understood. In this study, we comprehensively evaluated the variant calling performance of short- and long-read-based SNV, indel, and SV detection algorithms (6 for SNVs, 12 for indels, and 13 for SVs) using a novel evaluation framework incorporating manual visual inspection. The results showed that indel-insertion calls greater than 10 bp were poorly detected by short-read-based detection algorithms compared to long-read-based algorithms; however, the recall and precision of SNV and indel-deletion detection were similar between short- and long-read data. The recall of SV detection with short-read-based algorithms was significantly lower in repetitive regions, especially for small- to intermediate-sized SVs, than that detected with long-read-based algorithms. In contrast, the recall and precision of SV detection in nonrepetitive regions were similar between short- and long-read data. These findings suggest the need for refined strategies, such as incorporating multiple variant detection algorithms, to generate a more complete set of variants using short-read data.

Enhancing the Reliability of PMP22 Copy Number Variation Detection with an Inherited Peripheral Neuropathy Panel

The utility of the next-generation sequencing (NGS) panel could be increased in hereditary peripheral neuropathies, given that the duplication of PMP22 is a major abnormality. In the present study, the analytical performance of an algorithm for detecting PMP22 copy number variation (CNV) from the NGS panel data was evaluated. The NGS panel covers 141 genes, including PMP22 and five genes within 1.5-megabase duplicated region at 17p11.2. CNV calling was performed using a laboratory-developed algorithm. Among the 92 cases subjected to targeted NGS panel from March 2018 to January 2021, 26 were suggestive of PMP22 CNV. Multiplex ligation-dependent probe amplification analysis was performed in 58 cases, and the results were 100% concordant with the NGS data (23 duplications, 2 deletions, and 33 negatives). Analytical performance of the pipeline was further validated by another blind data set, including 14 positive and 20 negative samples. Reliable detection of PMP22 CNV was possible by analyzing not only PMP22 but also the adjacent genes within the 1.5-megabase region of 17p11.2. On the basis of the high accuracy of CNV calling for PMP22, the testing strategy for diagnosis of peripheral polyneuropathies could be simplified by reducing the need for multiplex ligation-dependent probe amplification.